To investigate whether distinct patterns of cortical involvement exist in Parkinson’s disease (PD) and to characterize their potential progression trajectories using multimodal neuroimaging and data-driven disease progression modeling. In this cross-sectional multimodal imaging study, we enrolled 317 patients with clinically diagnosed PD and 61 healthy controls. All participants underwent simultaneous FDG-PET and MRI scanning. We applied the Subtype and Stage Inference (SuStaIn) model to cortical glucose metabolism and thickness data to identify latent disease progression patterns. Network-level characteristics were further examined within a whole-brain gradient framework. Robustness was assessed through age- and sex-balanced sensitivity analyses in the local cohort and external validation using harmonized T1-weighted MRI data from the Parkinson’s Progression Markers Initiative (PPMI) dataset. Two distinct cortical involvement subtypes were identified. One subtype showed predominant alterations in higher-order association networks, including the default mode and frontoparietal networks, whereas the other was characterized by greater involvement of lower-order sensorimotor and limbic systems. These subtype patterns remained stable across sensitivity analyses and external validation. Disease duration showed a significant correlation with the inferred disease stage (r = 0.15, p = 0.01). Imaging findings further revealed hypermetabolism in brainstem and trans-entorhinal regions accompanied by widespread cortical hypometabolism. Our findings reveal two robust cortical progression patterns in PD, highlighting substantial heterogeneity in network-level metabolic and structural involvement. This framework provides new insights into PD phenotypic variability and may support future efforts toward disease stratification and personalized research.

Background Template-based PET metrics quantify Alzheimer disease (AD) amyloid-β (Aβ) and tau burden but compress whole-brain data into a single scalar, overlooking disease heterogeneity and sometimes causing imaging-clinical discordance. Artificial intelligence (AI) approaches capture richer patterns but often lack biologic interpretability. Purpose To develop and validate an interpretable deep-learning framework that separates AD-specific abnormalities from physiologic uptake using pathophysiologic constraints, generating a clinically meaningful AI biomarker. Materials and Methods In this retrospective study, Aβ and tau PET scans from the Alzheimer's Disease Neuroimaging Initiative, Australian Imaging Biomarkers and Lifestyle study, Global Alzheimer's Association Interactive Network, and the authors' center were analyzed. An adversarial decomposition learning (ADL) network generated voxel-level pathologic maps and an AD adversarial decomposition (ADAD) score. Discriminatory performance for clinical AD versus cognitively normal individuals was evaluated using the area under the curve (AUC). Clinical relevance was assessed with cognitive, hippocampal volume, cerebrospinal fluid (CSF), and neuropathologic measures using longitudinal mixed-effects models and Spearman correlations. Results The study included 7457 Aβ PET scans from 3595 patients (median age, 71.4 years; IQR, 65.7-77.0 years; 1637 female patients) and 1894 tau PET scans from 1127 patients (median age, 72.0 years; IQR, 66.9-78.5 years; 545 female patients). External testing AUCs were 0.94 (95% CI: 0.89, 0.98) for Aβ and 0.98 (95% CI: 0.95, 1.00) for tau. ADL generated interpretable pathologic attribution maps that correlated with expert rankings (Aβ and tau, Spearman ρ = 0.79 and 0.63, respectively). Although Centiloid and CenTauRz showed numerically higher correlations with postmortem neuropathologic structure and stronger associations with CSF biomarkers, the ADAD score demonstrated independent baseline and longitudinal associations with cognitive outcomes and hippocampal atrophy after adjustment. Conclusion Pathophysiologic-constrained ADL provided interpretable, personalized pathologic maps and an AI-derived ADAD score that more closely linked PET pathologic abnormalities with multimodal clinical measures. © RSNA, 2026 Supplemental material is available for this article.

The coexistence of primary osteosarcoma in the setting of primary hyperparathyroidism (PHPT) is exceedingly rare, and overlapping features between reactive brown tumors and malignancies pose a severe risk of misdiagnosis and diagnostic omission. We report the case of a 65-year-old female who presented with a left hip lesion. Although her clinical manifestations, imaging features, and initial pathology all favored a PHPT-induced brown tumor, a second pathology examination confirmed the coexistence of osteosarcoma within the pelvic lesion. In patients with PHPT, reactive giant cell hyperplasia and cystic degeneration can easily mask the malignant components of osteosarcoma. When encountering “red flags”, such as a solitary, extensive bone destruction that is disproportionate to parathyroid hormone (PTH) levels, clinicians must remain vigilant for a concurrent malignant bone tumor and promptly perform expanded sampling to prevent missed diagnoses. Multimodal imaging evaluations and, when necessary, repeated or expanded biopsies are also critical to avoiding misdiagnosis.

Semi-quantitative positron emission tomography (PET) analysis, particularly Centiloid and CenTauRz scaling, is essential for Alzheimer’s disease (AD) research and diagnosis. However, standard quantification workflows often depend on structural MRI for spatial normalization (SN) or rely on computationally intensive software, limiting clinical accessibility. In this retrospective, multi-center study (3539 patients; 6535 scans; 2005–2025), we compiled data across 7 modalities and 13 tracers to develop and validate the Deep Cascaded Cerebral Calculator (DCCC). This fully automated, PET-only framework employs cascaded CNN-based rigid/affine and VoxelMorph-based elastic registration modules for rapid SN. We benchmarked DCCC against the standard MRI-guided SPM12 pipeline and other PET-only tools using meta region-of-interest (ROI) standard uptake value ratio (SUVr) and correlation analyses. DCCC achieved a mean absolute relative SUVr error of 1.34±0.59% and a voxelwise Pearson correlation of 0.96±0.02, demonstrating robust generalization to unseen tracers and modalities including neuroinflammation and methionine metabolism imaging, with superior consistency compared to conventional template-based PET-only methods. Centiloid and CenTauRz estimates were highly accurate (R²>0.97) with a processing speed of 1.22±0.64 s per image. We further demonstrated DCCC’s utility across 3 scenarios: (1) longitudinal tracking, where it identified a distinct low-Centiloid AD subgroup; (2) deep learning preprocessing, yielding classification AUCs comparable to standard methods (P = 0.36); and (3) exploratory clinical support, where DCCC-derived metrics were adopted in 79% Aβ and 61% tau cases and were associated with changes in interpretation and increased agreement with reference labels in a multi-reader survey. Collectively, DCCC provides accurate, PET-only standardization, facilitating harmonized biomarker estimation without MRI and enabling large-scale, tracer-agnostic analyses in AD neuroimaging. A free standalone command-line interface program and a 3D Slicer plugin are provided.

Neuroinflammation is a key factor contributing to cognitive decline in Alzheimer’s disease (AD). This study aims to investigate the mechanistic associations among neuroinflammation, glymphatic dysfunction, tau pathology, and cognitive decline in AD spectrum. The study included 355 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and a supportive cohort of 59 individuals from Wuhan Union Hospital (WHUH). Tau pathology was quantified using 18F-AV1451 positron emission tomography (PET). Glymphatic function was estimated through diffusion tensor image analysis along the perivascular space (DTI-ALPS). Neuroinflammation was assessed via plasma glial fibrillary acidic protein (GFAP) in two cohorts and translocator protein (TSPO) PET imaging with 18F-DPA-714 in supportive cohort. Correlation analyses and mediation models were employed to evaluate the directional relationships among tau deposition, inflammation, glymphatic function, and cognition. Higher levels of inflammation were significantly associated with lower DTI-ALPS index (β = −0.171, P = 0.046), which in turn was associated with higher tau burden (β = 0.162, P = 0.010). Path analysis revealed significant indirect associations linking neuroinflammation to cognitive performance through glymphatic dysfunction and tau pathology, with total indirect effects of − 0.165 (95

The analysis of abnormalities in various brain regions requires combining metabolic data from PET with anatomical segmentation from MR due to the relatively low resolution of PET images. For brain MR segmentation, dealing with the intricate morphological characteristics of the Central Nervous System (CNS) represents a fundamental challenge in medical image analysis. While traditional methods such as Atlas-based techniques have been widely used, recent advancements in deep learning (DL) have significantly transformed the field. In this study, we automatically segmented two representative brain areas from hybrid PET/MR scans of twelve PD and three MSA patients using both Atlas- and DL-based methods. Then we compared the Standardized Uptake Values (SUVs) and accuracy of segmentation (DSC, MSD and HD) in the corresponding regions, with manual segmentation used as the ground truth for comparison. The Atlas-based brain segmentation relied on an atlas template from the automated anatomical labeling atlas, which includes 70 segmented regions labeled from 1 to 70. On the other hand, DL-based brain segmentation utilized a 3D transformer model based on MONAI framework for whole brain segmentation, employing a pre-trained model for inferring the whole brain with 133 structures on T1-weighted MR images. Both methods involved the use of SPM12 for the 3D affine registration of T1-weighted MR images to the MNI space. Manual segmentation was carried out by a clinical neuroimaging expert using ITK-SNAP, focusing on two cerebral nuclei containing four sub-regions (left Caudate, right Caudate, left Putamen, and right Putamen). The results of this study indicate that the DL-based method produced superior segmentation accuracy compared to the Atlas-based method. However, there were no significant differences in the SUVmax and SUVmean across the different methods for the segmentation of Caudate and Putamen. Despite being more accurate for Caudate and Putamen segmentation, the DL-based method had little effect on the calculation of their SUVs in hybrid PET/MR scans, as compared to the widely used Atlas-based method. This comparison and evaluation could potentially extend to other structures based on the algorithms.

We report the multimodal PET/MR imaging findings in a 31-year-old woman with a novel presenilin 1 (PSEN1) missense mutation (c.699G>A, p.M233I) who developed progressive cognitive decline and parkinsonian features. Multimodal imaging, including glucose metabolism, dopamine transporter function, amyloid-β pathology, and tau protein PET imaging, combined with structural MRI, revealed widespread abnormalities concordant with her clinical manifestations. Findings included glucose hypometabolism, dopaminergic dysfunction, amyloid and tau deposition, and cerebellar atrophy. This case highlights the diagnostic value of multiprobe PET/MR imaging in characterizing complex neurodegenerative phenotypes and expands the genotypic and phenotypic spectrum of PSEN1 -associated early-onset Alzheimer's disease (EOAD) with parkinsonism.

Purpose: To investigate and compare metabolic and perfusion alterations in temporal lobe epilepsy (TLE) patients via hybrid 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI). Methods: Twenty-one TLE patients (15 with left-sided TLE (LTLE) and 6 with right-sided TLE (RTLE)) who underwent brain 18F-FDG PET/MRI, and eight healthy controls (Hc) who had 18F-FDG PET/MRI for health examination, were included. Brain regions were segmented based on the automated anatomical labeling (AAL) template, and the hippocampus and temporal lobe were isolated for further analysis. Left and right sides of these structures were analyzed separately. Accordingly, the maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and cerebral blood flow (CBF) were compared between the two sides via paired t test. Asymmetry indexes (AI) were calculated and statistically compared between the TLE patients and Hc, along with PET and Arterial spin labeling (ASL)-derived AI. Results: LTLE patients showed significant asymmetrical differences in SUVmax, SUVmean, and CBF within the hippocampus region (p<0.01). In RTLE patients, only SUVmean showed significant asymmetrical in both the hippocampus (p=0.009) and temporal lobe (p=0.018). The PET-derived AI in the hippocampus nearly doubled in the TLE group compared to Hc group. Similarly, ASL-derived AI in the hippocampus also increased (7.22% vs 3.86%) in the TLE group compared to Hc group (p=0.051). In the temporal lobe, both PET and ASL-derived AIs increased in the TLE group; however, these increases were not statistically significant (p=0.260, p=0.364). In the hippocampus, a significant difference existed for the AI between PET and ASL (p=0.001), while the temporal lobe showed a significant correlation for the AI between PET and ASL (r=0.49, p=0.024). Conclusion: TLE patients exhibited distinct patterns of brain metabolism and perfusion between LTLE and RTLE. And the AIs derived from PET was more accurate than those of ASL in detecting abnormalities in the hippocampus. Meanwhile, metabolism and perfusion in TLE patients differed significantly in the hippocampus, while revealing a correlation in the temporal lobe.

Purpose Early-onset Alzheimer disease (EOAD) is rare, highly heterogeneous, and associated with poor prognosis. This AT(N) Framework–based study aimed to compare multiprobe PET/MRI findings between EOAD and late-onset Alzheimer disease (LOAD) patients and explore potential imaging biomarkers for characterizing EOAD. Methods Patients with AD who underwent PET/MRI in our PET center were retrospectively reviewed and grouped according to the age at disease onset: EOAD, younger than 60 years; and LOAD, 60 years or older. Clinical characteristics were recorded. All study patients had positive β-amyloid PET imaging; some patients also underwent 18 F-FDG and 18 F-florzolotau PET. Imaging of the EOAD and LOAD groups was compared using region-of-interest and voxel-based analysis. Correlation of onset age and regional SUV ratios were also evaluated. Results One hundred thirty-three patients were analyzed (75 EOAD and 58 LOAD patients). Sex ( P = 0.515) and education ( P = 0.412) did not significantly differ between groups. Mini-Mental State Examination score was significantly lower in the EOAD group (14.32 ± 6.74 vs 18.67 ± 7.20, P = 0.004). β-Amyloid deposition did not significantly differ between groups. Glucose metabolism in the frontal, parietal, precuneus, temporal, occipital lobe, and supramarginal and angular gyri was significantly lower in the EOAD group (n = 49) than in the LOAD group (n = 44). In voxel-based morphometry analysis, right posterior cingulate/precuneus atrophy was more obvious in the EOAD ( P < 0.001), although no voxel survived family-wise error correction. Tau deposition in the precuneus, parietal lobe, and angular, supramarginal, and right middle frontal gyri was significantly higher in the EOAD group (n = 18) than in the LOAD group (n = 13). Conclusions Multiprobe PET/MRI showed that tau burden and neuronal damage are more severe in EOAD than in LOAD. Multiprobe PET/MRI may be useful to assess the pathologic characteristics of EOAD.

Background Comparing to PET/CT, integrative PET/MRI imaging provides superior soft tissue resolution. This study aims to evaluate the added value of regional delayed F-18-FDG PET/MRI-assisted whole-body F-18-FDG PET/CT in diagnosing malignant ascites patients.Results The final diagnosis included 22 patients with ovarian cancer (n = 11), peritoneal cancer (n = 3), colon cancer (n = 2), liver cancer (n = 2), pancreatic cancer (n = 2), gastric cancer (n = 1), and fallopian tube cancer (n = 1). The diagnosis of the primary tumor using whole-body PET/CT was correct in 11 cases. Regional PET/MRI-assisted whole-body PET/CT diagnosis was correct in 18 cases, including 6 more cases of ovarian cancer and 1 more case of fallopian tube cancer. Among 4 cases that were not diagnosed correctly, 1 case had the primary tumor outside of the PET/MRI scan area, 2 cases were peritoneal cancer, and 1 case was colon cancer. The diagnostic accuracy of regional PET/MRI-assisted whole-body PET/CT was higher than PET/CT alone (81.8% vs. 50.0%, kappa (2) = 5.14, p = 0.023). The primary tumor conspicuity score of PET/MRI was higher than PET/CT (3.67 +/- 0.66 vs. 2.76 +/- 0.94, P < 0.01). In the same scan area, more metastases were detected in PET/MRI than in PET/CT (156 vs. 86 in total, and 7.43 +/- 5.17 vs. 4.10 +/- 1.92 per patient, t = 3.89, P < 0.01). Lesion-to-background ratio in PET/MRI was higher than that in PET/CT (10.76 +/- 5.16 vs. 6.56 +/- 3.45, t = 13.02, P < 0.01).Conclusion Comparing to whole-body PET/CT alone, additional delayed regional PET/MRI with high soft tissue resolution is helpful in diagnosing the location of the primary tumor and identifying more metastases in patients with malignant ascites. Yet larger sample size in multicenter and prospective clinical researches is still needed.

目的 观察多巴胺转运体和葡萄糖代谢PET/MRI鉴别诊断帕金森病(PD)、进行性核上性麻痹(PSP)Richardson综合征(PSP-RS)及PSP-帕金森综合征(PSP-P)的价值.方法 纳入34例PD(PD组)、19例PSP-RS(PSP-RS组)及15例PSP-P(PSP-P组)患者,采集各组11C-CFT和18F-FDG PET/MRI,基于体素水平分析组间结构MRI差异;基于图谱分割各脑区并获得标准摄取值比值(SUVR)、尾状核及壳核不对称指数(AI)及尾状核-壳核比(CPR),比较组间PET参数差异;应用logistic回归分析和受试者工作特征曲线评价各参数鉴别诊断PD与PSP的价值.结果 PSP-RS组、PSP-P组双侧尾状核及右侧壳核皮质、中脑及右侧额叶白质均显著萎缩(P均＜0.05),PD组双侧颞叶及右侧顶枕区白质显著萎缩(P均＜0.05)o11C-CFT PET 示 PD组双侧尾状核 SUVR 及 CPR 均高于 PSP-RS组及 PSP-P 组(P 均＜0.05).18F-FDG PET 示 PD组右侧额上回、右侧额下回及双侧尾状核SUVR均高于PSP-RS组及PSP-P组(P均＜0.05);PSP-RS组右侧丘脑、右侧小脑皮质、中脑及脑桥SUVR低于、而左侧颞横回SUVR高于PSP-P组(P均＜0.05).以结构MRI表现、11C-CFT PET参数、18F-FDG PET参数及上述三者联合临床特征鉴别PD与PSP的曲线下面积分别为0.868、0.853、0.978及0.973.结论 多模态多探针PET/MRI可有效鉴别PD与PSP,18F-FDG PET更可为鉴别PSP亚型提供可能.

IntroductionThis study aimed to investigate the feasibility of predicting progression-free survival (PFS) in breast cancer patients using pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomics signature and clinical parameters.MethodsBreast cancer patients who underwent 18F-FDG PET/CT imaging before treatment from January 2012 to December 2020 were eligible for study inclusion. Eighty-seven patients were randomly divided into training (n = 61) and internal test sets (n = 26) and an additional 25 patients were used as the external validation set. Clinical parameters, including age, tumor size, molecularsubtype, clinical TNM stage, and laboratory findings were collected. Radiomics features were extracted from preoperative PET/CT images. Least absolute shrinkage and selection operators were applied to shrink feature size and build a predictive radiomics signature. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to assess the association of rad-score and clinical parameter with PFS. Nomograms were constructed to visualize survival prediction. C-index and calibration curve were used to evaluate nomogram performance.ResultsEleven radiomics features were selected to generate rad-score. The clinical model comprised three parameters: clinical M stage, CA125, and pathological N stage. Rad-score and clinical-model were significantly associated with PFS in the training set (P< 0.01) but not the test set. The integrated clinical-radiomics (ICR) model was significantly associated with PFS in both the training and test sets (P< 0.01). The ICR model nomogram had a significantly higher C-index than the clinical model and rad-score in the training and test sets. The C-index of the ICR model in the external validation set was 0.754 (95% confidence interval, 0.726–0.812). PFS significantly differed between the low- and high-risk groups stratified by the nomogram (P = 0.009). The calibration curve indicated the ICR model provided the greatest clinical benefit.ConclusionThe ICR model, which combined clinical parameters and preoperative 18F-FDG PET/CT imaging, was able to independently predict PFS in breast cancer patients and was superior to the clinical model alone and rad-score alone.

Quantification of tau accumulation using positron emission tomography (PET) is critical for the diagnosis of Alzheimer’s disease (AD). This study aimed to evaluate the feasibility of 18F-florzolotau quantification in patients with AD using a magnetic resonance imaging (MRI)–free tau PET template, since individual high-resolution MRI is costly and not always available in practice. 18F-florzolotau PET and MRI scans were obtained in a discovery cohort including (1) patients within the AD continuum (n = 87), (2) cognitively impaired patients with non-AD (n = 32), and (3) cognitively unimpaired subjects (n = 26). The validation cohort comprised 24 patients with AD. Following MRI-dependent spatial normalization (standard approach) in randomly selected subjects (n = 40) to cover the entire spectrum of cognitive function, selected PET images were averaged to create the 18F-florzolotau-specific template. Standardized uptake value ratios (SUVRs) were calculated in five predefined regions of interest (ROIs). MRI-free and MRI-dependent methods were compared in terms of continuous and dichotomous agreement, diagnostic performances, and associations with specific cognitive domains. MRI-free SUVRs had a high continuous and dichotomous agreement with MRI-dependent measures for all ROIs (intraclass correlation coefficient ≥ 0.980; agreement ≥ 94.5

Purpose Early-onset Alzheimer disease (EOAD) is rare, highly heterogeneous, and associated with poor prognosis. This AT(N) Framework–based study aimed to compare multiprobe PET/MRI findings between EOAD and late-onset Alzheimer disease (LOAD) patients and explore potential imaging biomarkers for characterizing EOAD. Methods Patients with AD who underwent PET/MRI in our PET center were retrospectively reviewed and grouped according to the age at disease onset: EOAD, younger than 60 years; and LOAD, 60 years or older. Clinical characteristics were recorded. All study patients had positive β-amyloid PET imaging; some patients also underwent 18F-FDG and 18F-florzolotau PET. Imaging of the EOAD and LOAD groups was compared using region-of-interest and voxel-based analysis. Correlation of onset age and regional SUV ratios were also evaluated. Results One hundred thirty-three patients were analyzed (75 EOAD and 58 LOAD patients). Sex (P = 0.515) and education (P = 0.412) did not significantly differ between groups. Mini-Mental State Examination score was significantly lower in the EOAD group (14.32 ± 6.74 vs 18.67 ± 7.20, P = 0.004). β-Amyloid deposition did not significantly differ between groups. Glucose metabolism in the frontal, parietal, precuneus, temporal, occipital lobe, and supramarginal and angular gyri was significantly lower in the EOAD group (n = 49) than in the LOAD group (n = 44). In voxel-based morphometry analysis, right posterior cingulate/precuneus atrophy was more obvious in the EOAD (P < 0.001), although no voxel survived family-wise error correction. Tau deposition in the precuneus, parietal lobe, and angular, supramarginal, and right middle frontal gyri was significantly higher in the EOAD group (n = 18) than in the LOAD group (n = 13). Conclusions Multiprobe PET/MRI showed that tau burden and neuronal damage are more severe in EOAD than in LOAD. Multiprobe PET/MRI may be useful to assess the pathologic characteristics of EOAD.

Aim: Parkinson's disease is one of the most common neurodegenerative diseases. Excellent levodopa responsiveness has been proposed as a characteristic supporting feature in substantiating the PD diagnosis. However, a small portion of clinically established PD patients shows poor levodopa response. This study aims to investigate brain function alterations of PD patients with poor levodopa responsiveness by PET/MRI. Method: A total of 46 PD patients were recruited. They all completed C-11-CFT PET/MRI scans and the acute levodopa challenge test. Among these 46 PD patients, 42 participants further underwent F-18-FDG PET/MRI scans. Clinical variables regarding demographic data, disease features and cognition scales were also collected. Based on the improvement rate of UPDRS-III, PD patients were divided into non-responders (improvement rate < 33 %) and responders (improvement rate >= 33 %). Statistical parametric zapping was performed to analyze molecular imaging. Dopaminergic uptake and metabolism of 70 brain regions were converted to quantitative values and expressed as standard uptake value (SUV). SUV was further normalized by the cerebellum. The resulting SUV ratios and clinical variables were then compared by SPSS. Results: The difference between levodopa non-responders (n = 17) and responders (n = 29) in the UPDRS III baseline was statistically significant and the former had a lower UPDRS III baseline (19 (10, 32), p < 0.05). In contrast, no statistical difference between these two groups was found in age, gender, disease duration, cognition, motor subtype and Hoehn-Yahr stage. Dopaminergic uptake differences between levodopa non-responders (n = 17) and responders (n = 29) were shown in the left inferior frontal cortex (1.00 +/- 0.09 vs 1.07 +/- 0.08, p < 0.05 and FDR < 0.2), the right posterior cingulum (1.10 +/- 0.10 vs 1.20 +/- 0.13, p < 0.05 and FDR < 0.2) and the right insula (1.21 +/- 0.12 vs 1.30 +/- 0.10, p < 0.05 and FDR < 0.2). The metabolic alterations between levodopa non-responders (n = 16) and responders (n = 26) were shown in the right supplementary motor area (1.30 (1.18, 1.39) vs 1.41 (1.31, 1.53), p < 0.05 and FDR < 0.2), right precuneus (1.37 +/- 0.10 vs 1.47 +/- 0.18, p < 0.05 and FDR < 0.2), right parietal cortex (1.14 +/- 0.15 vs 1.27 +/- 0.21, p < 0.05 and FDR < 0.2), right supramarginal gyrus (1.16 (1.12, 1.26) vs 1.25 (1.14, 1.46), p < 0.05 and FDR < 0.2), right postcentral gyrus (1.15 (1.08, 1.32) vs 1.24 (1.17, 1.39), p < 0.05 and FDR < 0.2), medulla (0.75 +/- 0.07 vs 0.80 +/- 0.07, p < 0.05 and FDR < 0.2), right rolandic operculum (1.25 (1.18, 1.32) vs 1.33 (1.25, 1.50), p < 0.05 and FDR < 0.2), right olfactory (0.95 (0.91, 1.01) vs 1.01 (0.95, 1.15), p < 0.05 and FDR < 0.2), the right insula (1.15 (1.06, 1.22) vs 1.21 (1.12, 1.35), p < 0.05 and FDR < 0.2) and the left cerebellum crus (0.96 (0.91, 1.01) vs 0.92 (0.86, 0.96), p < 0.05 and FDR < 0.2). Conclusions: PD patients with poor response to levodopa showed less severe impairment of baseline motor symptoms, more severe dopaminergic deficits in the left inferior frontal, right posterior cingulate cortex and the right insula, and lower metabolism in the right supplementary motor area, right precuneus, right parietal cortex, right supramarginal gyrus, right postcentral gyrus, medulla, right rolandic operculum, right olfactory, the right insula and higher metabolism in the left cerebellum crus.